Paper: Robust Approach to Abbreviating Terms: A Discriminative Latent Variable Model with Global Information

ACL ID P09-1102
Title Robust Approach to Abbreviating Terms: A Discriminative Latent Variable Model with Global Information
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

The present paper describes a robust ap- proach for abbreviating terms. First, in order to incorporate non-local informa- tion into abbreviation generation tasks, we present both implicit and explicit solu- tions: the latent variable model, or alter- natively, the label encoding approach with global information. Although the two ap- proaches compete with one another, we demonstrate that these approaches are also complementary. By combining these two approaches, experiments revealed that the proposed abbreviation generator achieved the best results for both the Chinese and English languages. Moreover, we directly apply our generator to perform a very dif- ferent task from tradition, the abbreviation recognition. Experiments revealed that the proposed model worked robustly, and out- performe...